ENYS   25968
UNIDAD EJECUTORA DE ESTUDIOS EN NEUROCIENCIAS Y SISTEMAS COMPLEJOS
Unidad Ejecutora - UE
artículos
Título:
Modeling the effect of brain growth on cranial bones using finite-element analysis and geometric morphometrics
Autor/es:
BARBEITO-ANDRÉS, JIMENA; BERNAL, VALERIA; BONFILI, NOELIA; GONZALEZ, PAULA N.; NOGUÉ, JORDI MARCÉ
Revista:
SURGICAL AND RADIOLOGIC ANATOMY : SRA.
Editorial:
Springer
Referencias:
Año: 2020 vol. 42 p. 741 - 748
ISSN:
0930-1038
Resumen:
Purpose: Brain expansion during ontogeny has been identified as a key factor for explaining the growth pattern of neurocranial bones. However, the dynamics of this relation are only partially understood and a detailed characterization of integrated morphological changes of the brain and the neurocranium along ontogeny is still lacking. The aim of this study was to model the effect of brain growth on cranial bones by means of finite-element analysis (FEA) and geometric morphometric techniques. Methods: First, we described the postnatal changes in brain size and shape by digitizing coordinates of 3D semilandmarks on cranial endocasts, as a proxy of brain, segmented from CT-scans of an ontogenetic sample. Then, two scenarios of brain growth were simulated: one in which brain volume increases with the same magnitude in all directions, and other that includes the information on the relative expansion of brain regions obtained from morphometric analysis. Results: Results indicate that in the first model, in which a uniform pressure is applied, the largest displacements were localized in the sutures, especially in the anterior and posterior fontanels, as well as the metopic suture. When information of brain relative growth was introduced into the model, displacements were also concentrated in the lambda region although the values along both sides of the neurocranium (parietal and temporal bones) were larger than under the first scenario. Conclusion: In sum, we propose a realistic approach to the use of FEA based on morphometric data that offered different results to more simplified models.